{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "34qVD_ntSKLF"
   },
   "source": [
    "# Learning to optimize a set of 2D parametric nonlinear programming problems (pNLP) using Neuromancer.\n",
    "\n",
    "\n",
    "This is an interactive notebook based on the python script [Part_3_LearnToOptimize_pNLP.py](./Part_3_LearnToOptimize_pNLP.py).  \n",
    "\n",
    "Problem formulation:\n",
    "$$\n",
    "    \\begin{align}\n",
    "    &\\text{minimize } &&   f\\\\\n",
    "    &\\text{subject to} && \\left(\\frac{p}{2}\\right)^2 \\le x^2 + y^2 \\le p^2\\\\\n",
    "    & && x \\ge y\n",
    "    \\end{align}\n",
    "$$\n",
    "\n",
    "\n",
    "problem parameters:             $p$,  \n",
    "problem decition variables:     $x, y$,  \n",
    "Search domain:                  $-5.0 \\le x, y \\le 5.0$\n",
    "\n",
    "Set of objective functions f:  \n",
    "Rosenbrock:$ \\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,    f = (1 - x)^2 + (y - x^2)^2$  \n",
    "GomezLevy:$  \\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,      f = 4x^2 - 2.1x^4 + 1/3x^6 + xy - 4y^2 + 4y^4$  \n",
    "Himelblau:$  \\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,  f = (x^2 + y - 11)^2 + (x + y^2 - 7)^2$  \n",
    "Styblinski-Tang:$  \\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\, f = x^4 -15x^2 + 5x + y^4 -15y^2 + 5y$  \n",
    "Simionescu:$     \\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\, \\,\\,\\,\\,\\,\\,\\,\\,  f = 0.1xy$  \n",
    "McCormick:$       \\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\, f = sin(x + y) + (x - y)^2 - 1.5x + 2.5y +1$  \n",
    "Three-hump-camel:$  \\,\\,\\,\\,\\,\\,\\, f = 2x^2 - 1.05x^4 + (x^6)/6 + xy + y^2$  \n",
    "Beale:$               \\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\, \n",
    "   f = (1.5 - x + xy)^2 + (2.25 -x + xy^2)^2 + (2.625 -x + xy^3)^2$  \n",
    "\n",
    "See the description of the test problems here:  \n",
    "https://en.wikipedia.org/wiki/Test_functions_for_optimization  \n",
    "https://en.wikipedia.org/wiki/Rosenbrock_function  \n",
    "https://en.wikipedia.org/wiki/Himmelblau%27s_function  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OCn3zpaIqgMc"
   },
   "source": [
    "## NeuroMANCER and Dependencies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Qzy5Wot5k2Gf"
   },
   "source": [
    "### Install (Colab only)\n",
    "Skip this step when running locally."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: matplotlib in c:\\users\\drgo694\\anaconda3\\lib\\site-packages (3.5.1)\n",
      "Collecting matplotlib\n",
      "  Downloading matplotlib-3.7.1-cp39-cp39-win_amd64.whl (7.6 MB)\n",
      "Collecting contourpy>=1.0.1\n",
      "  Downloading contourpy-1.0.7-cp39-cp39-win_amd64.whl (160 kB)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\drgo694\\anaconda3\\lib\\site-packages (from matplotlib) (2.8.2)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in c:\\users\\drgo694\\anaconda3\\lib\\site-packages (from matplotlib) (3.0.4)\n",
      "Requirement already satisfied: numpy>=1.20 in c:\\users\\drgo694\\anaconda3\\lib\\site-packages (from matplotlib) (1.21.5)\n",
      "Requirement already satisfied: cycler>=0.10 in c:\\users\\drgo694\\anaconda3\\lib\\site-packages (from matplotlib) (0.11.0)\n",
      "Requirement already satisfied: packaging>=20.0 in c:\\users\\drgo694\\anaconda3\\lib\\site-packages (from matplotlib) (21.3)\n",
      "Collecting importlib-resources>=3.2.0\n",
      "  Downloading importlib_resources-5.12.0-py3-none-any.whl (36 kB)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\drgo694\\anaconda3\\lib\\site-packages (from matplotlib) (1.3.2)\n",
      "Requirement already satisfied: pillow>=6.2.0 in c:\\users\\drgo694\\anaconda3\\lib\\site-packages (from matplotlib) (9.0.1)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\drgo694\\anaconda3\\lib\\site-packages (from matplotlib) (4.25.0)\n",
      "Requirement already satisfied: zipp>=3.1.0 in c:\\users\\drgo694\\anaconda3\\lib\\site-packages (from importlib-resources>=3.2.0->matplotlib) (3.7.0)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\drgo694\\anaconda3\\lib\\site-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n",
      "Installing collected packages: importlib-resources, contourpy, matplotlib\n",
      "  Attempting uninstall: matplotlib\n",
      "    Found existing installation: matplotlib 3.5.1\n",
      "    Uninstalling matplotlib-3.5.1:\n",
      "      Successfully uninstalled matplotlib-3.5.1\n",
      "Successfully installed contourpy-1.0.7 importlib-resources-5.12.0 matplotlib-3.7.1\n"
     ]
    }
   ],
   "source": [
    "!pip install neuromancer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Note: When running on Colab, one might encounter a pip dependency error with Lida 0.0.10. This can be ignored*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LWyvndXlz0Fv"
   },
   "source": [
    "### Import"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "KbP0n-4evRqt",
    "ExecuteTime": {
     "end_time": "2025-06-26T18:46:51.582923Z",
     "start_time": "2025-06-26T18:46:38.467894Z"
    }
   },
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import numpy as np \n",
    "import neuromancer.slim as slim\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patheffects as patheffects\n",
    "from casadi import *\n",
    "import casadi"
   ],
   "outputs": [],
   "execution_count": 1
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "POL27EJZxJmI",
    "ExecuteTime": {
     "end_time": "2025-06-26T18:46:51.598740Z",
     "start_time": "2025-06-26T18:46:51.586940Z"
    }
   },
   "source": [
    "from neuromancer.trainer import Trainer\n",
    "from neuromancer.problem import Problem\n",
    "from neuromancer.constraint import variable\n",
    "from neuromancer.dataset import DictDataset\n",
    "from neuromancer.loss import PenaltyLoss, BarrierLoss, AugmentedLagrangeLoss\n",
    "from neuromancer.modules import blocks\n",
    "from neuromancer.system import Node\n",
    "import neuromancer.arg as arg"
   ],
   "outputs": [],
   "execution_count": 2
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_WH7o7Wu1epw"
   },
   "source": [
    "## Dataset"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "_r6p2p6myHAh",
    "ExecuteTime": {
     "end_time": "2025-06-26T18:46:51.741440Z",
     "start_time": "2025-06-26T18:46:51.726082Z"
    }
   },
   "source": [
    "data_seed = 408  # random seed used for simulated data\n",
    "np.random.seed(data_seed)\n",
    "torch.manual_seed(data_seed)"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<torch._C.Generator at 0x1f4d6fc0e30>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 3
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JZ9qrw0tlJhs"
   },
   "source": [
    "Randomly sample parameters from a uniform distribution: $0.5\\le p\\le6.0$"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "Nu58M-8JyHy6",
    "ExecuteTime": {
     "end_time": "2025-06-26T18:46:51.772629Z",
     "start_time": "2025-06-26T18:46:51.759089Z"
    }
   },
   "source": [
    "nsim = 5000  # number of datapoints: increase sample density for more robust results\n",
    "# create dictionaries with sampled datapoints with uniform distribution\n",
    "p_low, p_high = 0.5, 6.0\n",
    "samples_train = {\"p\": torch.FloatTensor(nsim, 1).uniform_(p_low, p_high)}\n",
    "samples_dev = {\"p\": torch.FloatTensor(nsim, 1).uniform_(p_low, p_high)}\n",
    "samples_test = {\"p\": torch.FloatTensor(nsim, 1).uniform_(p_low, p_high)}\n",
    "# create named dictionary datasets\n",
    "train_data = DictDataset(samples_train, name='train')\n",
    "dev_data = DictDataset(samples_dev, name='dev')\n",
    "test_data = DictDataset(samples_test, name='test')\n",
    "# create torch dataloaders for the Trainer\n",
    "train_loader = torch.utils.data.DataLoader(train_data, batch_size=32, num_workers=0,\n",
    "                                           collate_fn=train_data.collate_fn, shuffle=True)\n",
    "dev_loader = torch.utils.data.DataLoader(dev_data, batch_size=32, num_workers=0,\n",
    "                                         collate_fn=dev_data.collate_fn, shuffle=True)\n",
    "test_loader = torch.utils.data.DataLoader(test_data, batch_size=32, num_workers=0,\n",
    "                                         collate_fn=test_data.collate_fn, shuffle=True)"
   ],
   "outputs": [],
   "execution_count": 4
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Q6DqbTdL6S4o"
   },
   "source": [
    "## pNLP Formulation in NeuroMANCER"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Y2htUaWMDjsk"
   },
   "source": [
    "### Primal Solution Map Architecture"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "Ta_I_pjyyLzf",
    "ExecuteTime": {
     "end_time": "2025-06-26T18:46:51.804433Z",
     "start_time": "2025-06-26T18:46:51.790063Z"
    }
   },
   "source": [
    "n_hidden = 80                    # Number of hidden states of the solution map\n",
    "n_layers = 4                     # Number of hidden layers of the solution map\n",
    "# define neural architecture for the solution map\n",
    "func = blocks.MLP(insize=1, outsize=2,\n",
    "                linear_map=slim.maps['linear'],\n",
    "                nonlin=nn.ReLU,\n",
    "                hsizes=[n_hidden] * n_layers )\n",
    "# define symbolic solution map with concatenated features (problem parameters)\n",
    "sol_map = Node(func, ['p'], ['x'], name='map')"
   ],
   "outputs": [],
   "execution_count": 5
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Lxj77EFj7EO-"
   },
   "source": [
    "### Objective and Constraints in NeuroMANCER"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "bcoVjphjyPp9",
    "ExecuteTime": {
     "end_time": "2025-06-26T18:46:51.867543Z",
     "start_time": "2025-06-26T18:46:51.820093Z"
    }
   },
   "source": [
    "\"\"\"\n",
    "variable is a basic symbolic abstraction in Neuromancer\n",
    "   x = variable(\"variable_name\")                      (instantiates new variable)  \n",
    "variable construction supports:\n",
    "   algebraic expressions:     x**2 + x**3 + 5     (instantiates new variable)  \n",
    "   slicing:                   x[:, i]             (instantiates new variable)  \n",
    "   pytorch callables:         torch.sin(x)        (instantiates new variable)  \n",
    "   constraints definition:    x <= 1.0            (instantiates Constraint object) \n",
    "   objective definition:      x.minimize()        (instantiates Objective object) \n",
    "to visualize computational graph of the variable use x.show() method          \n",
    "\"\"\"\n",
    "\n",
    "# define weights for objective terms and constraints\n",
    "Q = 1.0                          # loss function weight\n",
    "Q_con = 100.0                    # constraints penalty weight\n",
    "\n",
    "# variables\n",
    "x = variable(\"x\")[:, [0]]\n",
    "y = variable(\"x\")[:, [1]]\n",
    "# sampled parameters\n",
    "p = variable('p')\n",
    "\n",
    "# list of nonlinear objective functions defined using Neuromancer variable\n",
    "obj_opt = {'Rosenbrock': (1 - x)**2 + (y - x**2)**2,\n",
    "           'GomezLevy': 4 * x ** 2 - 2.1 * x ** 4 + 1 / 3 * x ** 6 + x * y - 4 * y ** 2 + 4 * y ** 4,\n",
    "           'Himelblau': (x**2 + y - 11)**2 + (x + y**2 - 7)**2,\n",
    "           'Styblinski-Tang': x**4 -15*x**2 + 5*x + y**4 -15*y**2 + 5*y,\n",
    "           'Simionescu': 0.1*x*y,\n",
    "           'McCormick': torch.sin(x + y) + (x - y)**2 - 1.5*x + 2.5*y +1,\n",
    "           'Three-hump-camel': 2*x**2 - 1.05*x**4 + (x**6)/6 + x*y + y**2,\n",
    "           'Beale': (1.5 - x + x*y)**2 + (2.25 -x + x*y**2)**2 + (2.625 -x + x*y**3)**2 }\n",
    "\n",
    "# select objective function\n",
    "obj_fun = 'Himelblau'            # objective function to be optimized \n",
    "                                 #     obj_fun choices = ['Rosenbrock', 'GomezLevy', 'Himelblau', 'Styblinski-Tang', \n",
    "                                 #     'Simionescu', 'McCormick', 'Three-hump-camel', 'Beale'] \n",
    "f = obj_opt[obj_fun]\n",
    "obj = f.minimize(weight=Q, name='obj')\n",
    "\n",
    "# define constraints\n",
    "con_1 = Q_con*(x >= y)\n",
    "con_2 = Q_con*((p/2)**2 <= x**2+y**2)\n",
    "con_3 = Q_con*(x**2+y**2 <= p**2)\n",
    "con_1.name = 'c1'\n",
    "con_2.name = 'c2'\n",
    "con_3.name = 'c3'\n",
    "\n",
    "# lists of objective terms, constraints, and trainable components\n",
    "objectives = [obj]\n",
    "constraints = [con_1, con_2, con_3]\n",
    "components = [sol_map]"
   ],
   "outputs": [],
   "execution_count": 6
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 496
    },
    "id": "n7VPa9Wc8JRB",
    "outputId": "0da17c45-6370-4f46-f626-bd5686b94bfc",
    "ExecuteTime": {
     "end_time": "2025-06-26T18:46:51.899180Z",
     "start_time": "2025-06-26T18:46:51.883351Z"
    }
   },
   "source": [
    "# choose loss function type for optimization\n",
    "loss = 'penalty'                 # type of the loss function\n",
    "                                 #     loss choices = ['penalty', 'augmented_lagrange', 'barrier']\n",
    "barrier_type = 'log10'           # type of the barrier function to be used if loss = 'barrier' \n",
    "                                 #     barrier_type choices = ['log', 'log10', 'inverse']\n",
    "\n",
    "if loss == 'penalty':                        # penalty method\n",
    "    loss = PenaltyLoss(objectives, constraints)\n",
    "elif loss == 'barrier':                      # barrier method\n",
    "    loss = BarrierLoss(objectives, constraints, barrier=barrier_type)\n",
    "elif loss == 'augmented_lagrange':           # augmented Lagrangian method\n",
    "    optimizer_args = {'inner_loop': 1, \"eta\": 0.99, 'sigma': 2.0, 'mu_init': 1., \"mu_max\": 1000.}\n",
    "    loss = AugmentedLagrangeLoss(objectives, constraints, train_data, **optimizer_args)\n",
    "\n",
    "# construct constrained optimization problem\n",
    "problem = Problem(components, loss)"
   ],
   "outputs": [],
   "execution_count": 7
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "icWSMeG28SKc"
   },
   "source": [
    "## Parametric Problem Solution in NeuroMANCER"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "rk1bRczByUvl",
    "ExecuteTime": {
     "end_time": "2025-06-26T18:46:51.930926Z",
     "start_time": "2025-06-26T18:46:51.915198Z"
    }
   },
   "source": [
    "lr = 0.001      # step size for gradient descent\n",
    "epochs = 400    # number of training epochs\n",
    "warmup = 100    # number of epochs to wait before enacting early stopping policy\n",
    "patience = 100  # number of epochs with no improvement in eval metric to allow before early stopping"
   ],
   "outputs": [],
   "execution_count": 8
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "x4oS2N2ZyWtD",
    "ExecuteTime": {
     "end_time": "2025-06-26T18:46:51.962886Z",
     "start_time": "2025-06-26T18:46:51.949413Z"
    }
   },
   "source": [
    "optimizer = torch.optim.AdamW(problem.parameters(), lr=lr)\n",
    "\n",
    "# define trainer\n",
    "trainer = Trainer(\n",
    "    problem,\n",
    "    train_loader,\n",
    "    dev_loader,\n",
    "    test_loader,\n",
    "    optimizer,\n",
    "    epochs=epochs,\n",
    "    patience=patience,\n",
    "    warmup=warmup)"
   ],
   "outputs": [],
   "execution_count": 9
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "27ASQD3q-M0A",
    "outputId": "04eec20c-51c3-4a71-e570-9636af9421c0",
    "ExecuteTime": {
     "end_time": "2025-06-26T18:51:10.363570Z",
     "start_time": "2025-06-26T18:46:51.994840Z"
    }
   },
   "source": [
    "# Train NLP solution map\n",
    "best_model = trainer.train()\n",
    "best_outputs = trainer.test(best_model)\n",
    "# load best model dict\n",
    "problem.load_state_dict(best_model)"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 0  train_loss: 98.95836639404297\n",
      "epoch: 1  train_loss: 45.295928955078125\n",
      "epoch: 2  train_loss: 43.998626708984375\n",
      "epoch: 3  train_loss: 43.30885314941406\n",
      "epoch: 4  train_loss: 43.359214782714844\n",
      "epoch: 5  train_loss: 41.258941650390625\n",
      "epoch: 6  train_loss: 41.997501373291016\n",
      "epoch: 7  train_loss: 42.43018341064453\n",
      "epoch: 8  train_loss: 40.325157165527344\n",
      "epoch: 9  train_loss: 41.09640884399414\n",
      "epoch: 10  train_loss: 40.832454681396484\n",
      "epoch: 11  train_loss: 40.66997528076172\n",
      "epoch: 12  train_loss: 41.23598098754883\n",
      "epoch: 13  train_loss: 40.587520599365234\n",
      "epoch: 14  train_loss: 40.235626220703125\n",
      "epoch: 15  train_loss: 40.661170959472656\n",
      "epoch: 16  train_loss: 40.53839111328125\n",
      "epoch: 17  train_loss: 41.11820983886719\n",
      "epoch: 18  train_loss: 40.79458999633789\n",
      "epoch: 19  train_loss: 40.03028106689453\n",
      "epoch: 20  train_loss: 39.86722946166992\n",
      "epoch: 21  train_loss: 39.55671691894531\n",
      "epoch: 22  train_loss: 39.47555160522461\n",
      "epoch: 23  train_loss: 40.33986282348633\n",
      "epoch: 24  train_loss: 39.83742141723633\n",
      "epoch: 25  train_loss: 39.6917724609375\n",
      "epoch: 26  train_loss: 39.7703857421875\n",
      "epoch: 27  train_loss: 39.48335647583008\n",
      "epoch: 28  train_loss: 39.355628967285156\n",
      "epoch: 29  train_loss: 40.01911544799805\n",
      "epoch: 30  train_loss: 39.803009033203125\n",
      "epoch: 31  train_loss: 39.67717361450195\n",
      "epoch: 32  train_loss: 40.54037857055664\n",
      "epoch: 33  train_loss: 39.29792785644531\n",
      "epoch: 34  train_loss: 39.824462890625\n",
      "epoch: 35  train_loss: 39.616844177246094\n",
      "epoch: 36  train_loss: 39.51902389526367\n",
      "epoch: 37  train_loss: 40.06412887573242\n",
      "epoch: 38  train_loss: 38.87705993652344\n",
      "epoch: 39  train_loss: 39.63521194458008\n",
      "epoch: 40  train_loss: 39.08768081665039\n",
      "epoch: 41  train_loss: 39.66743087768555\n",
      "epoch: 42  train_loss: 38.8875732421875\n",
      "epoch: 43  train_loss: 39.31504440307617\n",
      "epoch: 44  train_loss: 39.42810821533203\n",
      "epoch: 45  train_loss: 40.22836685180664\n",
      "epoch: 46  train_loss: 39.93735885620117\n",
      "epoch: 47  train_loss: 39.86733627319336\n",
      "epoch: 48  train_loss: 38.99067687988281\n",
      "epoch: 49  train_loss: 39.20648956298828\n",
      "epoch: 50  train_loss: 39.001556396484375\n",
      "epoch: 51  train_loss: 38.651710510253906\n",
      "epoch: 52  train_loss: 39.17850875854492\n",
      "epoch: 53  train_loss: 39.377315521240234\n",
      "epoch: 54  train_loss: 40.18194580078125\n",
      "epoch: 55  train_loss: 40.03971481323242\n",
      "epoch: 56  train_loss: 39.271846771240234\n",
      "epoch: 57  train_loss: 38.853397369384766\n",
      "epoch: 58  train_loss: 38.68501281738281\n",
      "epoch: 59  train_loss: 39.24952697753906\n",
      "epoch: 60  train_loss: 38.0579948425293\n",
      "epoch: 61  train_loss: 38.507389068603516\n",
      "epoch: 62  train_loss: 39.147769927978516\n",
      "epoch: 63  train_loss: 38.723995208740234\n",
      "epoch: 64  train_loss: 38.48609924316406\n",
      "epoch: 65  train_loss: 38.593505859375\n",
      "epoch: 66  train_loss: 38.673763275146484\n",
      "epoch: 67  train_loss: 38.14488983154297\n",
      "epoch: 68  train_loss: 38.969295501708984\n",
      "epoch: 69  train_loss: 39.41050338745117\n",
      "epoch: 70  train_loss: 39.24892044067383\n",
      "epoch: 71  train_loss: 38.45334243774414\n",
      "epoch: 72  train_loss: 38.4197998046875\n",
      "epoch: 73  train_loss: 39.41825485229492\n",
      "epoch: 74  train_loss: 38.77597427368164\n",
      "epoch: 75  train_loss: 39.049339294433594\n",
      "epoch: 76  train_loss: 38.63402557373047\n",
      "epoch: 77  train_loss: 38.15901184082031\n",
      "epoch: 78  train_loss: 38.22628402709961\n",
      "epoch: 79  train_loss: 38.296390533447266\n",
      "epoch: 80  train_loss: 38.393394470214844\n",
      "epoch: 81  train_loss: 39.09283447265625\n",
      "epoch: 82  train_loss: 38.335323333740234\n",
      "epoch: 83  train_loss: 38.89921569824219\n",
      "epoch: 84  train_loss: 38.39445877075195\n",
      "epoch: 85  train_loss: 39.21418380737305\n",
      "epoch: 86  train_loss: 38.382869720458984\n",
      "epoch: 87  train_loss: 38.08771514892578\n",
      "epoch: 88  train_loss: 38.855411529541016\n",
      "epoch: 89  train_loss: 38.00215148925781\n",
      "epoch: 90  train_loss: 38.16358947753906\n",
      "epoch: 91  train_loss: 38.758182525634766\n",
      "epoch: 92  train_loss: 39.01649856567383\n",
      "epoch: 93  train_loss: 38.549415588378906\n",
      "epoch: 94  train_loss: 38.468299865722656\n",
      "epoch: 95  train_loss: 38.94343948364258\n",
      "epoch: 96  train_loss: 39.49196243286133\n",
      "epoch: 97  train_loss: 38.9443359375\n",
      "epoch: 98  train_loss: 39.00937271118164\n",
      "epoch: 99  train_loss: 38.42081069946289\n",
      "epoch: 100  train_loss: 38.26431655883789\n",
      "epoch: 101  train_loss: 38.81146240234375\n",
      "epoch: 102  train_loss: 38.920127868652344\n",
      "epoch: 103  train_loss: 38.70528793334961\n",
      "epoch: 104  train_loss: 38.074153900146484\n",
      "epoch: 105  train_loss: 38.19417953491211\n",
      "epoch: 106  train_loss: 38.107261657714844\n",
      "epoch: 107  train_loss: 38.75242233276367\n",
      "epoch: 108  train_loss: 38.42478942871094\n",
      "epoch: 109  train_loss: 38.69390106201172\n",
      "epoch: 110  train_loss: 38.52719497680664\n",
      "epoch: 111  train_loss: 39.39689636230469\n",
      "epoch: 112  train_loss: 37.70426559448242\n",
      "epoch: 113  train_loss: 38.525787353515625\n",
      "epoch: 114  train_loss: 38.136451721191406\n",
      "epoch: 115  train_loss: 39.230464935302734\n",
      "epoch: 116  train_loss: 38.69072341918945\n",
      "epoch: 117  train_loss: 38.21741485595703\n",
      "epoch: 118  train_loss: 38.77415466308594\n",
      "epoch: 119  train_loss: 38.83646774291992\n",
      "epoch: 120  train_loss: 38.26161575317383\n",
      "epoch: 121  train_loss: 38.22185516357422\n",
      "epoch: 122  train_loss: 38.97315216064453\n",
      "epoch: 123  train_loss: 38.76142883300781\n",
      "epoch: 124  train_loss: 38.51769256591797\n",
      "epoch: 125  train_loss: 38.50474548339844\n",
      "epoch: 126  train_loss: 38.26984405517578\n",
      "epoch: 127  train_loss: 39.12913513183594\n",
      "epoch: 128  train_loss: 38.389854431152344\n",
      "epoch: 129  train_loss: 38.78342819213867\n",
      "epoch: 130  train_loss: 38.658382415771484\n",
      "epoch: 131  train_loss: 38.38866424560547\n",
      "epoch: 132  train_loss: 38.3112678527832\n",
      "epoch: 133  train_loss: 38.376956939697266\n",
      "epoch: 134  train_loss: 38.15388488769531\n",
      "epoch: 135  train_loss: 39.414024353027344\n",
      "epoch: 136  train_loss: 39.093475341796875\n",
      "epoch: 137  train_loss: 38.226318359375\n",
      "epoch: 138  train_loss: 38.56309127807617\n",
      "epoch: 139  train_loss: 38.44682693481445\n",
      "epoch: 140  train_loss: 38.78025817871094\n",
      "epoch: 141  train_loss: 38.71308898925781\n",
      "epoch: 142  train_loss: 38.56590270996094\n",
      "epoch: 143  train_loss: 38.27574920654297\n",
      "epoch: 144  train_loss: 38.149070739746094\n",
      "epoch: 145  train_loss: 38.44970703125\n",
      "epoch: 146  train_loss: 38.862552642822266\n",
      "epoch: 147  train_loss: 38.34834289550781\n",
      "epoch: 148  train_loss: 38.73530197143555\n",
      "epoch: 149  train_loss: 38.874019622802734\n",
      "epoch: 150  train_loss: 38.41697311401367\n",
      "epoch: 151  train_loss: 38.18122863769531\n",
      "epoch: 152  train_loss: 38.45479202270508\n",
      "epoch: 153  train_loss: 38.82011795043945\n",
      "epoch: 154  train_loss: 38.4634895324707\n",
      "epoch: 155  train_loss: 38.28250503540039\n",
      "epoch: 156  train_loss: 38.58644104003906\n",
      "epoch: 157  train_loss: 38.66114044189453\n",
      "epoch: 158  train_loss: 38.81812286376953\n",
      "epoch: 159  train_loss: 38.07372283935547\n",
      "epoch: 160  train_loss: 38.22584533691406\n",
      "epoch: 161  train_loss: 38.507205963134766\n",
      "epoch: 162  train_loss: 38.31197738647461\n",
      "epoch: 163  train_loss: 38.47330856323242\n",
      "epoch: 164  train_loss: 38.29422378540039\n",
      "epoch: 165  train_loss: 38.20301818847656\n",
      "epoch: 166  train_loss: 37.71546173095703\n",
      "epoch: 167  train_loss: 38.191490173339844\n",
      "epoch: 168  train_loss: 38.204994201660156\n",
      "epoch: 169  train_loss: 38.092071533203125\n",
      "epoch: 170  train_loss: 38.797119140625\n",
      "epoch: 171  train_loss: 38.157772064208984\n",
      "epoch: 172  train_loss: 38.69414138793945\n",
      "epoch: 173  train_loss: 38.291683197021484\n",
      "epoch: 174  train_loss: 38.23780822753906\n",
      "epoch: 175  train_loss: 38.679744720458984\n",
      "epoch: 176  train_loss: 38.268409729003906\n",
      "epoch: 177  train_loss: 38.36676025390625\n",
      "epoch: 178  train_loss: 38.15083694458008\n",
      "epoch: 179  train_loss: 37.765010833740234\n",
      "epoch: 180  train_loss: 38.583824157714844\n",
      "epoch: 181  train_loss: 38.54910659790039\n",
      "epoch: 182  train_loss: 38.5242919921875\n",
      "epoch: 183  train_loss: 38.2061653137207\n",
      "epoch: 184  train_loss: 38.517982482910156\n",
      "epoch: 185  train_loss: 38.821414947509766\n",
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      "epoch: 187  train_loss: 38.172515869140625\n",
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      "epoch: 190  train_loss: 38.06675338745117\n",
      "epoch: 191  train_loss: 37.93111801147461\n",
      "epoch: 192  train_loss: 38.22364044189453\n",
      "epoch: 193  train_loss: 38.55326461791992\n",
      "epoch: 194  train_loss: 38.55048751831055\n",
      "epoch: 195  train_loss: 38.49027633666992\n",
      "epoch: 196  train_loss: 38.2613410949707\n",
      "epoch: 197  train_loss: 38.34001922607422\n",
      "epoch: 198  train_loss: 38.25706481933594\n",
      "epoch: 199  train_loss: 37.713016510009766\n",
      "epoch: 200  train_loss: 38.77409744262695\n",
      "epoch: 201  train_loss: 38.89337158203125\n",
      "epoch: 202  train_loss: 38.491355895996094\n",
      "epoch: 203  train_loss: 38.38166427612305\n",
      "epoch: 204  train_loss: 38.325523376464844\n",
      "epoch: 205  train_loss: 37.874393463134766\n",
      "epoch: 206  train_loss: 38.15871810913086\n",
      "epoch: 207  train_loss: 38.494483947753906\n",
      "epoch: 208  train_loss: 38.35614013671875\n",
      "epoch: 209  train_loss: 38.186275482177734\n",
      "epoch: 210  train_loss: 38.1780891418457\n",
      "epoch: 211  train_loss: 38.02838897705078\n",
      "epoch: 212  train_loss: 38.32569885253906\n",
      "epoch: 213  train_loss: 38.30417251586914\n",
      "epoch: 214  train_loss: 38.42185974121094\n",
      "epoch: 215  train_loss: 38.59333801269531\n",
      "epoch: 216  train_loss: 38.224571228027344\n",
      "epoch: 217  train_loss: 38.03884506225586\n",
      "epoch: 218  train_loss: 37.99946975708008\n",
      "epoch: 219  train_loss: 38.61545944213867\n",
      "epoch: 220  train_loss: 37.81867980957031\n",
      "epoch: 221  train_loss: 38.288818359375\n",
      "epoch: 222  train_loss: 38.21975326538086\n",
      "epoch: 223  train_loss: 38.11629104614258\n",
      "epoch: 224  train_loss: 38.4193229675293\n",
      "epoch: 225  train_loss: 38.31298828125\n",
      "epoch: 226  train_loss: 38.41220474243164\n",
      "epoch: 227  train_loss: 38.12552261352539\n",
      "epoch: 228  train_loss: 38.2198371887207\n",
      "epoch: 229  train_loss: 38.19924545288086\n",
      "epoch: 230  train_loss: 38.12872314453125\n",
      "epoch: 231  train_loss: 38.63151550292969\n",
      "epoch: 232  train_loss: 38.429866790771484\n",
      "epoch: 233  train_loss: 38.37866973876953\n",
      "epoch: 234  train_loss: 38.46064376831055\n",
      "epoch: 235  train_loss: 38.492801666259766\n",
      "epoch: 236  train_loss: 38.64689636230469\n",
      "epoch: 237  train_loss: 38.1103630065918\n",
      "epoch: 238  train_loss: 38.02396011352539\n",
      "epoch: 239  train_loss: 38.442840576171875\n",
      "epoch: 240  train_loss: 38.29100799560547\n",
      "epoch: 241  train_loss: 38.510353088378906\n",
      "epoch: 242  train_loss: 38.119022369384766\n",
      "epoch: 243  train_loss: 38.19136428833008\n",
      "epoch: 244  train_loss: 38.70508575439453\n",
      "epoch: 245  train_loss: 38.348243713378906\n",
      "epoch: 246  train_loss: 38.44255828857422\n",
      "epoch: 247  train_loss: 38.15306854248047\n",
      "epoch: 248  train_loss: 38.28124237060547\n",
      "epoch: 249  train_loss: 38.36505889892578\n",
      "epoch: 250  train_loss: 38.2402229309082\n",
      "epoch: 251  train_loss: 39.0029182434082\n",
      "epoch: 252  train_loss: 38.63980484008789\n",
      "epoch: 253  train_loss: 38.19685363769531\n",
      "epoch: 254  train_loss: 38.59662628173828\n",
      "epoch: 255  train_loss: 38.24524688720703\n",
      "Early stopping!!!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 10
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "g-KmqqUj-Q3E"
   },
   "source": [
    "## Get pNLP solution from CasADi for comparison\n",
    "\n",
    "[CasADi](https://web.casadi.org/) is an open-source tool for constrained optimization and optimal control that has influenced the development of NeuroMANCER."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "dmJERFP2yYuC",
    "ExecuteTime": {
     "end_time": "2025-06-26T18:51:10.539464Z",
     "start_time": "2025-06-26T18:51:10.509950Z"
    }
   },
   "source": [
    "# instantiate casadi optimizaiton problem class\n",
    "opti = casadi.Opti()\n",
    "# define variables\n",
    "x = opti.variable()\n",
    "y = opti.variable()\n",
    "p_opti = opti.parameter()\n",
    "# CasADi formulation of objectives\n",
    "obj_opt_cas = {'Rosenbrock': (1 - x) ** 2 + (y - x ** 2) ** 2,\n",
    "               'GomezLevy': 4 * x ** 2 - 2.1 * x ** 4 + 1 / 3 * x ** 6 + x * y - 4 * y ** 2 + 4 * y ** 4,\n",
    "               'Himelblau': (x ** 2 + y - 11) ** 2 + (x + y ** 2 - 7) ** 2,\n",
    "               'Styblinski-Tang': x ** 4 - 15 * x ** 2 + 5 * x + y ** 4 - 15 * y ** 2 + 5 * y,\n",
    "               'Simionescu': 0.1 * x * y,\n",
    "               'McCormick': np.sin(x + y) + (x - y) ** 2 - 1.5 * x + 2.5 * y + 1,\n",
    "               'Three-hump-camel': 2 * x ** 2 - 1.05 * x ** 4 + (x ** 6) / 6 + x * y + y ** 2,\n",
    "               'Beale': (1.5 - x + x * y) ** 2 + (2.25 - x + x * y ** 2) ** 2 + (2.625 - x + x * y ** 3) ** 2}\n",
    "f = obj_opt_cas[obj_fun]\n",
    "opti.minimize(f)\n",
    "# define objective and constraints\n",
    "opti.subject_to(x >= y)\n",
    "opti.subject_to((p_opti / 2) ** 2 <= x ** 2 + y ** 2)\n",
    "opti.subject_to(x ** 2 + y ** 2 <= p_opti ** 2)\n",
    "# select IPOPT solver and solve the NLP\n",
    "opti.solver('ipopt')"
   ],
   "outputs": [],
   "execution_count": 11
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-26T18:51:10.602555Z",
     "start_time": "2025-06-26T18:51:10.549695Z"
    }
   },
   "source": [
    "# set parametric value and solve a single instance NLP problem via CasADi\n",
    "p = 3.\n",
    "opti.set_value(p_opti, p)\n",
    "opti.solve()\n",
    "# solve NLP instance via CasADi\n",
    "sol = opti.solve()\n",
    "print('CasADi solution:')\n",
    "print(sol.value(x))\n",
    "print(sol.value(y))"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit https://github.com/coin-or/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "This is Ipopt version 3.14.11, running with linear solver MUMPS 5.4.1.\n",
      "\n",
      "Number of nonzeros in equality constraint Jacobian...:        0\n",
      "Number of nonzeros in inequality constraint Jacobian.:        6\n",
      "Number of nonzeros in Lagrangian Hessian.............:        3\n",
      "\n",
      "Total number of variables............................:        2\n",
      "                     variables with only lower bounds:        0\n",
      "                variables with lower and upper bounds:        0\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:        3\n",
      "        inequality constraints with only lower bounds:        1\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        2\n",
      "\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "   0  1.7000000e+02 2.25e+00 1.90e+01  -1.0 0.00e+00    -  0.00e+00 0.00e+00   0\n",
      "   1  1.6990428e+02 2.25e+00 1.01e+02  -1.0 2.27e+00   2.0 1.00e+00 9.80e-03h  1\n",
      "   2  1.4511199e+01 8.58e-01 1.33e+04  -1.0 2.85e+02   1.5 2.73e-04 7.81e-03f  8\n",
      "   3  3.9898531e+01 0.00e+00 2.07e+03  -1.0 6.01e+00    -  1.77e-03 1.00e+00h  1\n",
      "   4  3.7480913e+01 0.00e+00 4.63e+01  -1.0 5.91e-01    -  9.49e-01 1.00e+00f  1\n",
      "   5  2.5410747e+01 0.00e+00 8.28e+00  -1.0 7.45e-01   1.0 1.00e+00 1.00e+00f  1\n",
      "   6  1.0719664e+01 2.72e-01 5.57e+00  -1.0 5.14e+00    -  6.86e-01 3.71e-01f  1\n",
      "   7  1.0816005e+01 2.47e-01 4.73e+00  -1.0 1.76e-01    -  1.00e+00 1.51e-01h  1\n",
      "   8  1.2184497e+01 3.75e-04 2.98e-01  -1.0 1.54e-01    -  1.00e+00 1.00e+00h  1\n",
      "   9  1.2206617e+01 0.00e+00 1.50e-03  -1.7 1.31e-02    -  1.00e+00 1.00e+00h  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "  10  1.2186581e+01 0.00e+00 2.29e-05  -3.8 3.27e-03    -  1.00e+00 1.00e+00f  1\n",
      "  11  1.2186420e+01 0.00e+00 1.82e-09  -5.7 2.81e-05    -  1.00e+00 1.00e+00h  1\n",
      "  12  1.2186418e+01 0.00e+00 2.38e-13  -8.6 2.95e-07    -  1.00e+00 1.00e+00h  1\n",
      "\n",
      "Number of Iterations....: 12\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   1.2186418203843877e+01    1.2186418203843877e+01\n",
      "Dual infeasibility......:   2.3803181647963356e-13    2.3803181647963356e-13\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   2.5061924726088069e-09    2.5061924726088069e-09\n",
      "Overall NLP error.......:   2.5061924726088069e-09    2.5061924726088069e-09\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 19\n",
      "Number of objective gradient evaluations             = 13\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 22\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 13\n",
      "Number of Lagrangian Hessian evaluations             = 12\n",
      "Total seconds in IPOPT                               = 0.013\n",
      "\n",
      "EXIT: Optimal Solution Found.\n",
      "      solver  :   t_proc      (avg)   t_wall      (avg)    n_eval\n",
      "       nlp_f  |        0 (       0)  26.00us (  1.37us)        19\n",
      "       nlp_g  |        0 (       0)  73.00us (  3.32us)        22\n",
      "  nlp_grad_f  |        0 (       0)  37.00us (  2.64us)        14\n",
      "  nlp_hess_l  |        0 (       0) 298.00us ( 24.83us)        12\n",
      "   nlp_jac_g  |        0 (       0)  60.00us (  4.29us)        14\n",
      "       total  |  12.00ms ( 12.00ms)  13.45ms ( 13.45ms)         1\n",
      "This is Ipopt version 3.14.11, running with linear solver MUMPS 5.4.1.\n",
      "\n",
      "Number of nonzeros in equality constraint Jacobian...:        0\n",
      "Number of nonzeros in inequality constraint Jacobian.:        6\n",
      "Number of nonzeros in Lagrangian Hessian.............:        3\n",
      "\n",
      "Total number of variables............................:        2\n",
      "                     variables with only lower bounds:        0\n",
      "                variables with lower and upper bounds:        0\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:        3\n",
      "        inequality constraints with only lower bounds:        1\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        2\n",
      "\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "   0  1.7000000e+02 2.25e+00 1.90e+01  -1.0 0.00e+00    -  0.00e+00 0.00e+00   0\n",
      "   1  1.6990428e+02 2.25e+00 1.01e+02  -1.0 2.27e+00   2.0 1.00e+00 9.80e-03h  1\n",
      "   2  1.4511199e+01 8.58e-01 1.33e+04  -1.0 2.85e+02   1.5 2.73e-04 7.81e-03f  8\n",
      "   3  3.9898531e+01 0.00e+00 2.07e+03  -1.0 6.01e+00    -  1.77e-03 1.00e+00h  1\n",
      "   4  3.7480913e+01 0.00e+00 4.63e+01  -1.0 5.91e-01    -  9.49e-01 1.00e+00f  1\n",
      "   5  2.5410747e+01 0.00e+00 8.28e+00  -1.0 7.45e-01   1.0 1.00e+00 1.00e+00f  1\n",
      "   6  1.0719664e+01 2.72e-01 5.57e+00  -1.0 5.14e+00    -  6.86e-01 3.71e-01f  1\n",
      "   7  1.0816005e+01 2.47e-01 4.73e+00  -1.0 1.76e-01    -  1.00e+00 1.51e-01h  1\n",
      "   8  1.2184497e+01 3.75e-04 2.98e-01  -1.0 1.54e-01    -  1.00e+00 1.00e+00h  1\n",
      "   9  1.2206617e+01 0.00e+00 1.50e-03  -1.7 1.31e-02    -  1.00e+00 1.00e+00h  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "  10  1.2186581e+01 0.00e+00 2.29e-05  -3.8 3.27e-03    -  1.00e+00 1.00e+00f  1\n",
      "  11  1.2186420e+01 0.00e+00 1.82e-09  -5.7 2.81e-05    -  1.00e+00 1.00e+00h  1\n",
      "  12  1.2186418e+01 0.00e+00 2.38e-13  -8.6 2.95e-07    -  1.00e+00 1.00e+00h  1\n",
      "\n",
      "Number of Iterations....: 12\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   1.2186418203843877e+01    1.2186418203843877e+01\n",
      "Dual infeasibility......:   2.3803181647963356e-13    2.3803181647963356e-13\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   2.5061924726088069e-09    2.5061924726088069e-09\n",
      "Overall NLP error.......:   2.5061924726088069e-09    2.5061924726088069e-09\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 19\n",
      "Number of objective gradient evaluations             = 13\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 22\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 13\n",
      "Number of Lagrangian Hessian evaluations             = 12\n",
      "Total seconds in IPOPT                               = 0.009\n",
      "\n",
      "EXIT: Optimal Solution Found.\n",
      "      solver  :   t_proc      (avg)   t_wall      (avg)    n_eval\n",
      "       nlp_f  |        0 (       0)  22.00us (  1.16us)        19\n",
      "       nlp_g  |        0 (       0)  36.00us (  1.64us)        22\n",
      "  nlp_grad_f  |        0 (       0)  25.00us (  1.79us)        14\n",
      "  nlp_hess_l  |        0 (       0)  51.00us (  4.25us)        12\n",
      "   nlp_jac_g  |        0 (       0)  33.00us (  2.36us)        14\n",
      "       total  |   9.00ms (  9.00ms)   9.80ms (  9.80ms)         1\n",
      "CasADi solution:\n",
      "2.6118493832297625\n",
      "1.4758871531798778\n"
     ]
    }
   ],
   "execution_count": 12
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-26T18:51:10.677604Z",
     "start_time": "2025-06-26T18:51:10.660820Z"
    }
   },
   "source": [
    "# solve NLP instance via Neuromancer\n",
    "datapoint = {'p': torch.tensor([[p]]), 'name': 'test'}\n",
    "model_out = problem(datapoint)\n",
    "x_nm = model_out['test_' + \"x\"][0, 0].detach().numpy()\n",
    "y_nm = model_out['test_' + \"x\"][0, 1].detach().numpy()\n",
    "print('Neuromancer solution:')\n",
    "print(x_nm)\n",
    "print(y_nm)"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Neuromancer solution:\n",
      "2.6294835\n",
      "1.4328706\n"
     ]
    }
   ],
   "execution_count": 13
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pcNq5fOVE4lR"
   },
   "source": [
    "## Visual comparison: NeuroMANCER vs. CasADi"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 281
    },
    "id": "kvYCfjq6zxxC",
    "outputId": "322e5b72-b93d-4d9e-a913-8703aad67d1a",
    "ExecuteTime": {
     "end_time": "2025-06-26T18:51:14.572467Z",
     "start_time": "2025-06-26T18:51:10.752015Z"
    }
   },
   "source": [
    "x1 = np.arange(-5., 5., 0.02)\n",
    "y1 = np.arange(-5., 5., 0.02)\n",
    "xx, yy = np.meshgrid(x1, y1)\n",
    "# eval and plot objective\n",
    "obj_opt_plot = {'Rosenbrock': (1 - xx) ** 2 + (yy - xx ** 2) ** 2,\n",
    "               'GomezLevy': 4 * xx ** 2 - 2.1 * xx ** 4 + 1 / 3 * xx ** 6 + xx * yy - 4 * yy ** 2 + 4 * yy ** 4,\n",
    "               'Himelblau': (xx ** 2 + yy - 11) ** 2 + (xx + yy ** 2 - 7) ** 2,\n",
    "               'Styblinski-Tang': xx ** 4 - 15 * xx ** 2 + 5 * xx + yy ** 4 - 15 * yy ** 2 + 5 * yy,\n",
    "               'Simionescu': 0.1 * xx * yy,\n",
    "               'McCormick': np.sin(xx + yy) + (xx - yy) ** 2 - 1.5 * xx + 2.5 * yy + 1,\n",
    "               'Three-hump-camel': 2 * xx ** 2 - 1.05 * xx ** 4 + (xx ** 6) / 6 + xx * yy + yy ** 2,\n",
    "               'Beale': (1.5 - xx + xx * yy) ** 2 + (2.25 - xx + xx * yy ** 2) ** 2 + (2.625 - xx + xx * yy ** 3) ** 2}\n",
    "J = obj_opt_plot[obj_fun]\n",
    "fig, ax = plt.subplots(1, 1)\n",
    "cp = ax.contourf(xx, yy, J, levels=1000, alpha=0.6)\n",
    "fig.colorbar(cp)\n",
    "ax.set_title(obj_fun+' pNLP')\n",
    "# eval  and plot  constraints\n",
    "c1 = xx - yy\n",
    "c2 = xx ** 2 + yy ** 2 - (p / 2) ** 2\n",
    "c3 = -(xx ** 2 + yy ** 2) + p ** 2\n",
    "\n",
    "cg1 = ax.contour(xx, yy, c1, [0], colors='mediumblue', alpha=0.7)\n",
    "if hasattr(cg1, 'collections') and cg1.collections:\n",
    "    plt.setp(cg1.collections,\n",
    "            path_effects=[patheffects.withTickedStroke()], alpha=0.7)\n",
    "\n",
    "cg2 = ax.contour(xx, yy, c2, [0], colors='mediumblue', alpha=0.7)\n",
    "if hasattr(cg2, 'collections') and cg2.collections:\n",
    "    plt.setp(cg2.collections,\n",
    "            path_effects=[patheffects.withTickedStroke()], alpha=0.7)\n",
    "\n",
    "cg3 = ax.contour(xx, yy, c3, [0], colors='mediumblue', alpha=0.7)\n",
    "if hasattr(cg3, 'collections') and cg3.collections:\n",
    "    plt.setp(cg3.collections,\n",
    "            path_effects=[patheffects.withTickedStroke()], alpha=0.7)\n",
    "\n",
    "# plot optimal solutions CasADi vs Neuromancer\n",
    "ax.plot(sol.value(x), sol.value(y), 'g*', markersize=10, label='CasADi')\n",
    "ax.plot(x_nm, y_nm, 'r*', fillstyle='none', markersize=10, label='NeuroMANCER')\n",
    "plt.legend(bbox_to_anchor=(1.0, 0.15))\n",
    "plt.show(block=True)\n"
   ],
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\drgon\\AppData\\Local\\Temp\\ipykernel_56028\\2986408025.py:23: MatplotlibDeprecationWarning: The collections attribute was deprecated in Matplotlib 3.8 and will be removed in 3.10.\n",
      "  plt.setp(cg1.collections,\n",
      "C:\\Users\\drgon\\AppData\\Local\\Temp\\ipykernel_56028\\2986408025.py:26: MatplotlibDeprecationWarning: The collections attribute was deprecated in Matplotlib 3.8 and will be removed in 3.10.\n",
      "  plt.setp(cg2.collections,\n",
      "C:\\Users\\drgon\\AppData\\Local\\Temp\\ipykernel_56028\\2986408025.py:29: MatplotlibDeprecationWarning: The collections attribute was deprecated in Matplotlib 3.8 and will be removed in 3.10.\n",
      "  plt.setp(cg3.collections,\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 14
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-26T18:51:14.748730Z",
     "start_time": "2025-06-26T18:51:14.734363Z"
    }
   },
   "source": [],
   "outputs": [],
   "execution_count": null
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "display_name": "neuromancer",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
